Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging.

Moritz Gross,John A Onofrey,Julius Chapiro,Simon Iseke,Ariel Jaffe,Michael Spektor,Stefan P Haider,Mario Strazzabosco,Ahmet S Kucukkaya

doi:10.1371/journal.pone.0260630

Abstract

Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection algorithms. Liver segmentation can be especially challenging in patients with cancer-related tissue changes and shape deformation. The aim of this study was to assess the ability of state-of-the-art deep learning 3D liver segmentation algorithms to generalize across all different Barcelona Clinic Liver Cancer (BCLC) liver cancer stages. This retrospective study, included patients from an institutional database that had arterial-phase T1-weighted magnetic resonance images with corresponding manual liver segmentations. The data was split into 70/15/15% for training/validation/testing each proportionally equal across BCLC stages. Two 3D convolutional neural networks were trained using identical U-net-derived architectures with equal sized training datasets: one spanning all BCLC stages ("All-Stage-Net": AS-Net), and one limited to early and intermediate BCLC stages ("Early-Intermediate-Stage-Net": EIS-Net). Segmentation accuracy was evaluated by the Dice Similarity Coefficient (DSC) on a dataset spanning all BCLC stages and a Wilcoxon signed-rank test was used for pairwise comparisons. 219 subjects met the inclusion criteria (170 males, 49 females, 62.8±9.1 years) from all BCLC stages. Both networks were trained using 129 subjects: AS-Net training comprised 19, 74, 18, 8, and 10 BCLC 0, A, B, C, and D patients, respectively; EIS-Net training comprised 21, 86, and 22 BCLC 0, A, and B patients, respectively. DSCs (mean±SD) were 0.954±0.018 and 0.946±0.032 for AS-Net and EIS-Net (p<0.001), respectively. The AS-Net 0.956±0.014 significantly outperformed the EIS-Net 0.941±0.038 on advanced BCLC stages (p<0.001) and yielded similarly good segmentation performance on early and intermediate stages (AS-Net: 0.952±0.021; EIS-Net: 0.949±0.027; p = 0.107). To ensure robust segmentation performance across cancer stages that is independent of liver shape deformation and tumor burden, it is critical to train deep learning models on heterogeneous imaging data spanning all BCLC stages.

Highlights

Liver cancer is the third most common cause of cancer-related death worldwide [1] and both incidence rates and mortality are rising [2, 3]
To ensure robust segmentation performance across cancer stages that is independent of liver shape deformation and tumor burden, it is critical to train deep learning models on heterogeneous imaging data spanning all Barcelona Clinic Liver Cancer (BCLC) stages
The aim of this study was to assess the ability of state-of-the-art deep learning 3D liver segmentation algorithms to generalize across the clinical distribution all different BCLC liver cancer stages

Summary

Introduction

Liver cancer is the third most common cause of cancer-related death worldwide [1] and both incidence rates and mortality are rising [2, 3]. Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer, accounting for 70–85% of liver cancers globally [4]. The Barcelona Clinic Liver Cancer (BCLC) staging classification [6] is widely accepted and the most commonly used in Western cohorts. The BCLC classification utilizes three clinical elements: tumor burden, functional status as measured by the Eastern Cooperative Oncology Group (ECOG) Performance Status [11], and underlying liver function measured by the Child-Pugh class [12] to stratify patients into five staging categories: very early stage (BCLC-0), early stage (BCLC-A), intermediate stage (BCLC-B), advanced stage (BCLC-C), and terminal stage (BCLC-D)

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Conclusion